Shier Nee Saw

434 total citations
25 papers, 290 citations indexed

About

Shier Nee Saw is a scholar working on Obstetrics and Gynecology, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Shier Nee Saw has authored 25 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Obstetrics and Gynecology, 9 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Biomedical Engineering. Recurrent topics in Shier Nee Saw's work include Pregnancy and preeclampsia studies (9 papers), Birth, Development, and Health (5 papers) and Ultrasound Imaging and Elastography (4 papers). Shier Nee Saw is often cited by papers focused on Pregnancy and preeclampsia studies (9 papers), Birth, Development, and Health (5 papers) and Ultrasound Imaging and Elastography (4 papers). Shier Nee Saw collaborates with scholars based in Malaysia, Singapore and United Kingdom. Shier Nee Saw's co-authors include Kwan Hoong Ng, Choon Hwai Yap, Citra Nurfarah Zaini Mattar, Arijit Biswas, Alys R. Clark, Joanna L. James, Win Tun, Hwee Kuan Lee, Chu Kiong Loo and Martin L. Buist and has published in prestigious journals such as Scientific Reports, IEEE Access and Journal of Biomechanics.

In The Last Decade

Shier Nee Saw

22 papers receiving 288 citations

Peers

Shier Nee Saw

RNMI

PPCH

Yasuhisa Kurata Japan

Yudi Geng China

Min Sun Kyung South Korea

Dhakshinamoorthy Ganeshan United States

Alex Mottrie Belgium

Yann Chaussy France

Stefano Granato Italy

Yongping Lu China

Rosa Castillo United States

Christopher T. Lam United States

Yasuhisa Kurata Japan

Shier Nee Saw

219 ×1.9

152 ×1.8

RNMI

25 ×0.3

PPCH

30 ×0.6

11 ×0.3

Citations per year, relative to Shier Nee Saw Shier Nee Saw (= 1×) peers Yasuhisa Kurata

Countries citing papers authored by Shier Nee Saw

Since Specialization

Citations

This map shows the geographic impact of Shier Nee Saw's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shier Nee Saw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shier Nee Saw more than expected).

Fields of papers citing papers by Shier Nee Saw

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shier Nee Saw. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shier Nee Saw. The network helps show where Shier Nee Saw may publish in the future.

Co-authorship network of co-authors of Shier Nee Saw

This figure shows the co-authorship network connecting the top 25 collaborators of Shier Nee Saw. A scholar is included among the top collaborators of Shier Nee Saw based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shier Nee Saw. Shier Nee Saw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Sethi, Neha, Azanna Ahmad Kamar, Rahmah Saaid, et al.. (2025). Enhancing Small‐for‐Gestational‐Age Prediction: Multi‐Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models. Prenatal Diagnosis. 45(3). 374–386.

Phan, Chia‐Wei, et al.. (2025). Current Knowledge on Mushroom and Mushroom-Based Product Authentication: From DNA Barcoding, Chemometrics, to Artificial Intelligence. Food Reviews International. 41(8). 2372–2387. 2 indexed citations

Tan, Yu‐Meng, Shireen Anne Nah, Shier Nee Saw, Retnagowri Rajandram, & Teng Aik Ong. (2025). Evaluating the performance of artificial intelligence chatbots in answering urology questions derived from guidelines or board examinations: A systematic review. Urological Science. 1 indexed citations

Saw, Shier Nee, et al.. (2024). Current status and future directions of explainable artificial intelligence in medical imaging. European Journal of Radiology. 183. 111884–111884. 11 indexed citations

Huang, Junjun, et al.. (2024). A Reconstructed UNet Model With Hybrid Fuzzy Pooling for Gastric Cancer Segmentation in Tissue Pathology Images. IEEE Transactions on Fuzzy Systems. 33(1). 457–467. 1 indexed citations

Huang, Junjun, et al.. (2023). A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0. Journal of Industrial Information Integration. 34. 100467–100467. 8 indexed citations

Mao, Bin-Hsu, et al.. (2023). A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images. Materials Today Bio. 23. 100820–100820. 7 indexed citations

Saw, Shier Nee, et al.. (2023). The accuracy of international and national fetal growth charts in detecting small-for-gestational-age infants using the Lambda-Mu-Sigma method. Frontiers in Surgery. 10. 1123948–1123948. 1 indexed citations

Saw, Shier Nee, et al.. (2023). Machine Learning Methods for Neonatal Heart Rate Prediction using Respiratory Signals. 334–338.

10.

Li, Xuan, et al.. (2023). A Miniaturized Ultrasonic Micro-Hole Perforator for Minimally Invasive Craniotomy. IEEE Transactions on Biomedical Engineering. 70(7). 2069–2079. 3 indexed citations

11.

Mattar, Citra Nurfarah Zaini, et al.. (2022). Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning. Scientific Reports. 12(1). 3907–3907. 14 indexed citations

12.

Saw, Shier Nee & Kwan Hoong Ng. (2022). Current challenges of implementing artificial intelligence in medical imaging. Physica Medica. 100. 12–17. 63 indexed citations

13.

Saw, Shier Nee, et al.. (2021). A Review of Biomechanics Analysis of the Umbilical–Placenta System With Regards to Diseases. Frontiers in Physiology. 12. 587635–587635. 16 indexed citations

14.

Saw, Shier Nee, Arijit Biswas, Citra Nurfarah Zaini Mattar, Hwee Kuan Lee, & Choon Hwai Yap. (2021). Machine learning improves early prediction of small‐for‐gestational‐age births and reveals nuchal fold thickness as unexpected predictor. Prenatal Diagnosis. 41(4). 505–516. 9 indexed citations

15.

Tun, Win, Choon Hwai Yap, Shier Nee Saw, Joanna L. James, & Alys R. Clark. (2019). Differences in placental capillary shear stress in fetal growth restriction may affect endothelial cell function and vascular network formation. Scientific Reports. 9(1). 9876–9876. 42 indexed citations

16.

Saw, Shier Nee, et al.. (2018). Hyperelastic Mechanical Properties of Ex Vivo Normal and Intrauterine Growth Restricted Placenta. Annals of Biomedical Engineering. 46(7). 1066–1077. 9 indexed citations

17.

Saw, Shier Nee, et al.. (2018). Characterization of the hemodynamic wall shear stresses in human umbilical vessels from normal and intrauterine growth restricted pregnancies. Biomechanics and Modeling in Mechanobiology. 17(4). 1107–1117. 13 indexed citations

18.

Saw, Shier Nee, Liying Yang, Alys R. Clark, et al.. (2018). Altered Placental Chorionic Arterial Biomechanical Properties During Intrauterine Growth Restriction. Scientific Reports. 8(1). 16526–16526. 18 indexed citations

19.

Saw, Shier Nee, et al.. (2018). Motorizing and Optimizing Ultrasound Strain Elastography for Detection of Intrauterine Growth Restriction Pregnancies. Ultrasound in Medicine & Biology. 44(3). 532–543. 10 indexed citations

20.

Saw, Shier Nee, et al.. (2016). Characterization of the in vivo wall shear stress environment of human fetus umbilical arteries and veins. Biomechanics and Modeling in Mechanobiology. 16(1). 197–211. 34 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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